Fabrication and Evaluation of a Novel Non-Invasive Stretchable and Wearable Respiratory Rate Sensor Based on Silver Nanoparticles Using Inkjet Printing Technology

The respiration rate (RR) is a key vital sign that links to adverse clinical outcomes and has various important uses. However, RR signals have been neglected in many clinical practices for several reasons and it is still difficult to develop low-cost RR sensors for accurate, automated, and continuous measurement. This study aims to fabricate, develop and evaluate a novel stretchable and wearable RR sensor that is low-cost and easy to use. The sensor is fabricated using the soft lithography technique of polydimethylsiloxane substrates (PDMS) for the stretchable sensor body and inkjet printing technology for creating the conductive circuit by depositing the silver nanoparticles on top of the PDMS substrates. The inkjet-printed (IJP) PDMS-based sensor was developed to detect the inductance fluctuations caused by respiratory volumetric changes. The output signal was processed in a Wheatstone bridge circuit to derive the RR. Six different patterns for a IJP PDMS-based sensor were carefully designed and tested. Their sustainability (maximum strain during measurement) and durability (the ability to go bear axial cyclic strains) were investigated and compared on an automated mechanical stretcher. Their repeatability (output of the sensor in repeated tests under identical condition) and reproducibility (output of different sensors with the same design under identical condition) were investigated using a respiratory simulator. The selected optimal design pattern from the simulator evaluation was used in the fabrication of the IJP PDMS-based sensor where the accuracy was inspected by attaching it to 37 healthy human subjects (aged between 19 and 34 years, seven females) and compared with the reference values from e-Health nasal sensor. Only one design survived the inspection procedures where design #6 (array consists of two horseshoe lines) indicated the best sustainability and durability, and went through the repeatability and reproducibility tests. Based on the best pattern, the developed sensor accurately measured the simulated RR with an error rate of 0.46 ± 0.66 beats per minute (BPM, mean ± SD). On human subjects, the IJP PDMS-based sensor and the reference e-Health sensor showed the same RR value, without any observable differences. The performance of the sensor was accurate with no apparent error compared with the reference sensor. Considering its low cost, good mechanical property, simplicity, and accuracy, the IJP PDMS-based sensor is a promising technique for continuous and wearable RR monitoring, especially under low-resource conditions

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

Conductive Cellulose Composites with Low Percolation Threshold for 3D Printed Electronics

We are reporting a 3D printable composite paste having strong thixotropic rheology. The composite has been designed and investigated with highly conductive silver nanowires. The optimized electrical percolation threshold from both simulation and experiment is shown from 0.7 vol. % of silver nanowires which is significantly lower than other composites using conductive nano-materials. Reliable conductivity of 1.19 × 102 S/cm has been achieved from the demonstrated 3D printable composite with 1.9 vol. % loading of silver nanowires. Utilizing the high conductivity of the printable composites, 3D printing of designed battery electrode pastes is demonstrated. Rheology study shows superior printability of the electrode pastes aided by the cellulose\u27s strong thixotropic rheology. The designed anode, electrolyte, and cathode pastes are sequentially printed to form a three-layered lithium battery for the demonstration of a charging profile. This study opens opportunities of 3D printable conductive materials to create printed electronics with the next generation additive manufacturing process

Tunneling Conductivity and Piezoresistivity of Composites Containing Randomly Dispersed Conductive Nano-Platelets

In this study, a three-dimensional continuum percolation model was developed based on a Monte Carlo simulation approach to investigate the percolation behavior of an electrically insulating matrix reinforced with conductive nano-platelet fillers. The conductivity behavior of composites rendered conductive by randomly dispersed conductive platelets was modeled by developing a three-dimensional finite element resistor network. Parameters related to the percolation threshold and a power-low describing the conductivity behavior were determined. The piezoresistivity behavior of conductive composites was studied employing a reoriented resistor network emulating a conductive composite subjected to mechanical strain. The effects of the governing parameters, i.e., electron tunneling distance, conductive particle aspect ratio and size effects on conductivity behavior were examined